Abstract

We use monthly observations on general stock price indices, over January 2001–August 2013, in order to assesssimple stochastic time series models in terms of out-of-sample forecasts. Specifically, we examine the relativestrength of out-of-sample forecasts of a random walk, with and without drift, against that of a non-linearsegmented trends model where the switch between states is governed by a Markov chain. The forecastingperformance of these processes is assessed by the root mean squared error of short- and long-term out-of-sampleforecasts, varying from 1- to 12-month horizons. We obtain compelling evidence in favor of the Markovswitching process in forecasting stock prices over short and medium-term horizons and across all countriesconsidered. These results are most likely due to risk averse behavior of investors which has been amplified bythe recent financial crisis.

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